Question

Forecasting labour costs is a key aspect of hotel revenue management that enables hoteliers to appropriately allocate hotel resources and fix pricing strategies. Mary, the President of Hellenic Hoteliers Federation (HHF) is interested in investigating how labour costs (variable L_COST) relate to the number of rooms in a hotel (variable Total_Rooms). Suppose that HHF has hired you as a business analyst to develop a linear model to predict hotel labour costs based on the total number of rooms per hotel using the data provided. 3.1 Use the least squares method to estimate the regression coefficients b0 and b1 3.2 State the regression equation 3.3 Plot on the same graph, the scatter diagram and the regression line 3.4 Give the interpretation of the regression coefficients b0 and b1 as well as the result of the t-test on the individual variables (assume a significance level of 5%) Determine the correlation coefficient of the two variables and provide an interpretation of its meaning in the context of this problem.Check statistically, at the 0.05 level of significance whether there is any evidence of a linear relationship between labour cost and total number of rooms per hotel

STARS	Total_Rooms	Region_ID	ARR_MAY	ARR_AUG	L_COST
5	412	1	95	160	2.165.000
5	313	1	94	173	2.214.985
5	265	1	81	174	1.393.550
5	204	1	131	225	2.460.634
5	172	1	90	195	1.151.600
5	133	1	71	136	801.469
5	127	1	85	114	1.072.000
4	322	1	70	159	1.608.013
4	241	1	64	109	793.009
4	172	1	68	148	1.383.854
4	121	1	64	132	494.566
4	70	1	59	128	437.684
4	65	1	25	63	83.000
3	93	1	76	130	626.000
3	75	1	40	60	37.735
3	69	1	60	70	256.658
3	66	1	51	65	230.000
3	54	1	65	90	200.000
2	68	1	45	55	199.000
1	57	1	35	90	11.720
4	38	1	22	51	59.200
4	27	1	70	100	130.000
3	47	1	60	120	255.020
3	32	1	40	60	3.500
3	27	1	48	55	20.906
2	48	1	52	60	284.569
2	39	1	53	104	107.447
2	35	1	80	110	64.702
2	23	1	40	50	6.500
1	25	1	59	128	156.316
4	10	1	90	105	15.950
3	18	1	94	104	722.069
2	17	1	29	53	6.121
2	29	1	26	44	30.000
1	21	1	42	54	5.700
1	23	1	30	35	50.237
2	15	1	47	50	19.670
1	8	1	31	49	7.888
1	15	1	40	55	3.500
1	18	1	35	40	112.181
4	10	1	57	97	30.000
2	26	1	35	40	3.575
5	306	2	113	235	2.074.000
5	240	2	61	132	1.312.601
5	330	2	112	240	434.237
5	139	2	100	130	495.000
4	353	2	87	152	1.511.457
4	324	2	112	211	1.800.000
4	276	2	95	160	2.050.000
4	221	2	47	102	623.117
4	200	2	77	178	796.026
4	117	2	48	91	360.000
3	170	2	60	104	538.848
3	122	2	25	33	568.536
5	57	2	68	140	300.000
4	62	2	55	75	249.205
3	98	2	38	75	150.000
3	75	2	45	70	220.000
3	62	2	45	90	50.302
5	50	2	100	180	517.729
4	27	2	180	250	51.000
3	44	2	38	84	75.704
3	33	2	99	218	271.724
3	25	2	45	95	118.049
2	30	2	30	55	40.000
3	10	2	40	70	10.000
2	18	2	60	100	10.000
2	73	2	22	41	70.000
2	21	2	55	100	12.000
1	22	2	40	100	20.000
1	25	2	80	120	36.277
1	25	2	80	120	36.277
1	31	2	18	35	10.450
3	16	2	80	100	14.300
2	15	2	30	45	4.296
1	16	2	25	70	379.498
1	22	2	30	35	1.520
4	12	2	215	265	45.000
4	34	2	133	218	96.619
2	37	2	35	95	270.000
2	25	2	100	150	60.000
2	10	2	70	100	12.500
5	270	3	60	90	1.934.820
5	261	3	119	211	3.000.000
5	219	3	93	162	1.675.995
5	280	3	81	138	903.000
5	378	3	44	128	2.429.367
5	181	3	100	187	1.143.850
5	166	3	98	183	900.000
5	119	3	100	150	600.000
5	174	3	102	211	2.500.000
5	124	3	103	160	1.103.939
4	112	3	40	56	363.825
4	227	3	69	123	1.538.000
4	161	3	112	213	1.370.968
4	216	3	80	124	1.339.903
3	102	3	53	91	173.481
4	96	3	73	134	210.000
4	97	3	94	120	441.737
4	56	3	70	100	96.000
3	72	3	40	75	177.833
3	62	3	50	90	252.390
3	78	3	70	120	377.182
3	74	3	80	95	111.000
3	33	3	85	120	238.000
3	30	3	50	80	45.000
3	39	3	30	68	50.000
3	32	3	30	100	40.000
2	25	3	32	55	61.766
2	41	3	50	90	166.903
2	24	3	70	120	116.056
2	49	3	30	73	41.000
2	43	3	94	120	195.821
2	20	3	70	120	96.713
2	32	3	19	45	6.500
2	14	3	35	70	5.500
2	14	3	50	80	4.000
1	13	3	25	45	15.000
1	13	3	30	50	9.500
2	53	3	55	80	48.200
3	11	3	95	120	3.000
1	16	3	25	31	27.084
1	21	3	16	40	30.000
1	21	3	16	40	20.000
1	46	3	19	23	43.549
1	21	3	30	40	10.000

Answer 1